Artificial intelligence-based image analysis and data analysis for flare prevention in psoriasis and psoriatic arthritis

Artificial intelligence-based image analysis and data analysis for flare prevention in psoriasis and psoriatic arthritis - DDS

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00037907

Enrollment

200

Registered

2025-09-15

Start date

2022-03-31

Completion date

Unknown

Last updated

2025-10-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

L40

Interventions

Group 1: A total of 200 patients are planned to be included, consisting primarily of dermatologically managed patients with psoriasis vulgaris and rheumatologically managed patients with psoriatic art

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Presence of moderate to severe psoriasis with or without psoriatic arthritis Capacity to provide informed consent, age over 18 years, possession of a smartphone

Exclusion criteria

Exclusion criteria: Lack of capacity to provide informed consent Age under 18 years No possession of a smartphone

Design outcomes

Primary

Measure	Time frame
Primary study objectives: Prediction of new disease flares through AI- and AutoML-based image, movement, and data analyses a. Creation of a digital dataset of patients with psoriasis and psoriatic arthritis b. Training of machine learning algorithms with the above-mentioned digital dataset to generate predictions and optimize processes c. Identification of non-linear relationships between patient disease data and disease course or treatment response Analysis of current psoriasis and psoriatic arthritis management within the framework of app usage and the interdisciplinary outpatient clinic a. Assessment of therapy compliance and treatment satisfaction (measured using a compliance questionnaire and a visual analogue scale in the app) b. Evaluation of the Patient Benefit Index (PBI) Analysis of treatment outcomes and patient care for psoriasis and psoriatic arthritis within the framework of app usage and the interdisciplinary outpatient clinic a. Extent of skin and joint involvement b. Itch c. Pain d. Quality of life e. Psychological well-being of patients i. measured using patient-reported outcome scores (see below)	—

Measure

Time frame

Primary study objectives: Prediction of new disease flares through AI- and AutoML-based image, movement, and data analyses a. Creation of a digital dataset of patients with psoriasis and psoriatic arthritis b. Training of machine learning algorithms with the above-mentioned digital dataset to generate predictions and optimize processes c. Identification of non-linear relationships between patient disease data and disease course or treatment response Analysis of current psoriasis and psoriatic arthritis management within the framework of app usage and the interdisciplinary outpatient clinic a. Assessment of therapy compliance and treatment satisfaction (measured using a compliance questionnaire and a visual analogue scale in the app) b. Evaluation of the Patient Benefit Index (PBI) Analysis of treatment outcomes and patient care for psoriasis and psoriatic arthritis within the framework of app usage and the interdisciplinary outpatient clinic a. Extent of skin and joint involvement b. Itch c. Pain d. Quality of life e. Psychological well-being of patients i. measured using patient-reported outcome scores (see below)

—

Secondary

Measure	Time frame
Evaluation of the app and the interdisciplinary outpatient clinic by patients using free-text feedback and multiple-choice questions. Provision of a decision basis regarding the feasibility of a future subsequent clinical trial aimed at obtaining approval of the app as a Class I medical device. a. This is a pure feasibility study, within which the following aspects will be assessed: i. organizational implementation ii. technical feasibility iii. practical applicability iv. resources and availability of medical staff for patient care via the app	—

Measure

Time frame

Evaluation of the app and the interdisciplinary outpatient clinic by patients using free-text feedback and multiple-choice questions. Provision of a decision basis regarding the feasibility of a future subsequent clinical trial aimed at obtaining approval of the app as a Class I medical device. a. This is a pure feasibility study, within which the following aspects will be assessed: i. organizational implementation ii. technical feasibility iii. practical applicability iv. resources and availability of medical staff for patient care via the app

—

Countries

Germany

Contacts

Public ContactAstrid Schmieder

Universitätklinikum Würzburg, Dermatologie

schmieder_a@ukw.de093120126710

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Mar 1, 2026